Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach

Pousinho, Hugo Miguel Inácio; Mendes, V.M.F.; Catalão, João P.S.

doi:10.1016/j.ijepes.2012.01.001

Cited by 98 publications

(53 citation statements)

References 32 publications

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“…This comparative table clearly strengthens the fact that the ARIMA-based two-stage model is a promising forecasting method to improve the accuracies in residual training for short-term price forecasting. [47] 13.39 Neural Network-40 days [48] 11.40 Weighted Nearest Neighbor-23 months [49] 10.89 Wavelet-ARIMA with 4 Variables-47 days [50] 10.70 Fuzzy Neural Network [51] 9.84 Adaptive Wavelet Neural Network with 2 variables [52] 9.64 Neural network Wavelet Transform with 1 variable [53] 9.5 WNF with 1 variable-42 days [54] 9.47 Elman Network [55] 9.09 Hybrid Intelligent systems with 3 Variables 7.47 Wavelet-ARIMA-RBFN 6.76 Hybrid wavelet-PSO-ANFIS [56] 6.50 Cascaded Neuro-evolutionary Algorithm with 2 variables-50 days [57] 5.79 Table 9 categorizes the MAPE results as good, average and bad for easy classification of readers. MAPE results between 1-4.99% are termed as good results, while MAPE results between 5-9.99% is classified as average results.…”

Section: Mapementioning

confidence: 99%

A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

et al. 2018

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Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.

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Section: Mapementioning

confidence: 99%

A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

et al. 2018

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“…When the other data sets are used, comparison is performed over the test sets. In the comparison of the methods, mean square error (MSE) calculated for the training sets while root mean square error (RMSE) given in (11), mean absolute percentage error (MAPE) given in (12), median absolute percentage error (MdAPE) given in (13) and direction accuracy (DA) given in (14) are calculated for the test sets.…”

Section: The Applicationsmentioning

confidence: 99%

“…Lohani et al [10] compared ANFIS with autoregressive model and multilayer perceptron for river flow data. Particle swarm optimization was used to obtain membership function optimal parameters in the study of Pousinho et al [11]. Cheng [12] proposed OWA based ANFIS.…”

Section: Introductionmentioning

confidence: 99%

High order fuzzy time series forecasting method based on an intersection operation

Yolcu

Eğrioğlu

et al. 2016

Applied Mathematical Modelling

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1 Highlights  A novel high-order fuzzy time series method is suggested.  In the proposed method, intersection operators are utilized to deal with the excessive number of inputs.  Feed forward neural network is used to determine fuzzy relations. Fuzzy c-means method is employed for fuzzification.  The membership values are used to determine fuzzy relations instead of using cluster numbers.  The superior performance of proposed method is demonstrated in real world applications and a simulation study. ABSTRACTUsing non-stochastic models such as fuzzy time series forecasting models for time series analysis has been attracted attention by researchers in recent years. Fuzzy time series forecasting models do not need strict assumptions. However, conventional stochastic models need to satisfy some assumptions. In addition, fuzzy time series methods can be used if the observations of time series have uncertainty. Fuzzy time series approaches consist of three basic steps. These steps are; fuzzification of the crisp observations, identification of fuzzy relations and defuzzification, respectively. In the literature, there are many methods proposed by researchers that contribute to all these stages to obtain more accurate forecasting results. One of the weak features of fuzzy time series methods is that the membership values are not taken into consideration in forecasting process. This problem was eliminated for first order approaches by taking advantage of artificial neural networks to describe fuzzy relations. While determining of fuzzy relations, if inputs and outputs of the neural networks are membership values for the period t-1 and t respectively, the membership values are not ignored. However, if this approach is extended for high-order models, the number of inputs of neural networks will increase strongly. So, it will be very hard to train such of neural networks. In this study, a novel high-order fuzzy time series approach in which membership values are taken into account and artificial neural networks are employed to identify fuzzy relations is proposed. In the proposed method, intersection operators are utilized to deal with the excessive number of inputs. Also, fuzzy c-means method is employed for fuzzification. The evaluation of forecasting performance is performed by application the proposed method to well-known time series data sets and the obtained results are compared with those produced by other forecasting methods available in the literature. In addition, the superior performance of proposed method is supported by a simulation study.

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“…This is really difficult for electricity price forecasting handlers. To solve this issue, an intelligent optimization algorithm is usually used to automatically determine the parameters of artificial intelligent forecasting models, such as particle swam optimization (PSO) [19,20] and genetic algorithm (GA) [21,22].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting

Guo

Zhao

2017

Information

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Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND) method, fruit fly optimization algorithm (FOA), and least square support vector machine (LSSVM) model, namely BND-FOA-LSSVM model. Firstly, the original electricity price time series were decomposed into deterministic term, periodic term, and stochastic term by using BND model. Then, these three decomposed terms were forecasted by employing LSSVM model, respectively. Meanwhile, to improve the forecasting performance, a new swarm intelligence optimization algorithm FOA was used to automatically determine the optimal parameters of LSSVM model for deterministic term forecasting, periodic term forecasting, and stochastic term forecasting. Finally, the forecasting result of electricity price can be obtained by multiplying the forecasting values of these three terms. The results show the mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed BND-FOA-LSSVM model are respectively 3.48%, 11.18 Yuan/MWh and 9.95 Yuan/MWh, which are much smaller than that of LSSVM, BND-LSSVM, FOA-LSSVM, auto-regressive integrated moving average (ARIMA), and empirical mode decomposition (EMD)-FOA-LSSVM models. The proposed BND-FOA-LSSVM model is effective and practical for electricity price forecasting, which can improve the electricity price forecasting accuracy.

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Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach

Cited by 98 publications

References 32 publications

A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

High order fuzzy time series forecasting method based on an intersection operation

A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting

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